TUDataset: A collection of benchmark datasets for learning with graphs

Abstract

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.

ICML 2020 workshop "Graph Representation Learning and Beyond"

References in corpus (4)

• Fast Graph Representation Learning with PyTorch Geometric
• What's in a crowd? Analysis of face-to-face behavioral networks
• Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
• IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Cited by in corpus (26)

• Adversarial Graph Augmentation to Improve Graph Contrastive Learning
• Federated Graph Classification over Non-IID Graphs
• Graph Kernels: State-of-the-Art and Future Challenges
• Graph Pooling via Coarsened Graph Infomax
• Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
• Higher-order Clustering and Pooling for Graph Neural Networks
• Edge Representation Learning with Hypergraphs
• A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
• Large-scale graph representation learning with very deep GNNs and self-supervision
• ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
• Adversarial Attacks on Graph Classification via Bayesian Optimisation
• An Empirical Study of Graph Contrastive Learning
• Learning to Pool in Graph Neural Networks for Extrapolation
• Learning Graphons via Structured Gromov-Wasserstein Barycenters
• A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
• Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits
• Should Graph Neural Networks Use Features, Edges, Or Both?
• Embedding Graphs on Grassmann Manifold
• Edge but not Least: Cross-View Graph Pooling
• Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
• Diversified Multiscale Graph Learning with Graph Self-Correction
• Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
• Towards a Taxonomy of Graph Learning Datasets
• Scaling up graph homomorphism for classification via sampling
• Graph2Graph Learning with Conditional Autoregressive Models
• Learning Graphon Autoencoders for Generative Graph Modeling